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Elastic Docs ›Elasticsearch Guide [7.8] ›Set up Elasticsearch ›Configuring Elasticsearch

Thread pools

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Thread pools

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A node uses several thread pools to manage memory consumption. Queues associated with many of the thread pools enable pending requests to be held instead of discarded.

There are several thread pools, but the important ones include:

generic
For generic operations (for example, background node discovery). Thread pool type is scaling.
search
For count/search/suggest operations. Thread pool type is fixed_auto_queue_size with a size of int((# of allocated processors * 3) / 2) + 1, and initial queue_size of 1000.
search_throttled
For count/search/suggest/get operations on search_throttled indices. Thread pool type is fixed_auto_queue_size with a size of 1, and initial queue_size of 100.
get
For get operations. Thread pool type is fixed with a size of # of allocated processors, queue_size of 1000.
analyze
For analyze requests. Thread pool type is fixed with a size of 1, queue size of 16.
write
For single-document index/delete/update and bulk requests. Thread pool type is fixed with a size of # of allocated processors, queue_size of 200. The maximum size for this pool is 1 + # of allocated processors.
snapshot
For snapshot/restore operations. Thread pool type is scaling with a keep-alive of 5m and a max of min(5, (# of allocated processors) / 2).
warmer
For segment warm-up operations. Thread pool type is scaling with a keep-alive of 5m and a max of min(5, (# of allocated processors) / 2).
refresh
For refresh operations. Thread pool type is scaling with a keep-alive of 5m and a max of min(10, (# of allocated processors) / 2).
listener
Mainly for java client executing of action when listener threaded is set to true. Thread pool type is scaling with a default max of min(10, (# of allocated processors) / 2).
fetch_shard_started
For listing shard states. Thread pool type is scaling with keep-alive of 5m and a default maximum size of 2 * # of allocated processors.
fetch_shard_store
For listing shard stores. Thread pool type is scaling with keep-alive of 5m and a default maximum size of 2 * # of allocated processors.
flush
For flush, synced flush, and translog fsync operations. Thread pool type is scaling with a keep-alive of 5m and a default maximum size of min(5, ( # of allocated processors) / 2).
force_merge
For force merge operations. Thread pool type is fixed with a size of 1 and an unbounded queue size.
management
For cluster management. Thread pool type is scaling with a keep-alive of 5m and a default maximum size of 5.

Changing a specific thread pool can be done by setting its type-specific parameters; for example, changing the number of threads in the write thread pool:

thread_pool:
    write:
        size: 30

Thread pool types

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The following are the types of thread pools and their respective parameters:

fixed

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The fixed thread pool holds a fixed size of threads to handle the requests with a queue (optionally bounded) for pending requests that have no threads to service them.

The size parameter controls the number of threads.

The queue_size allows to control the size of the queue of pending requests that have no threads to execute them. By default, it is set to -1 which means its unbounded. When a request comes in and the queue is full, it will abort the request.

thread_pool:
    write:
        size: 30
        queue_size: 1000

fixed_auto_queue_size

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This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.

deprecated[7.7.0,The experimental fixed_auto_queue_size thread pool type is deprecated and will be removed in 8.0.]

The fixed_auto_queue_size thread pool holds a fixed size of threads to handle the requests with a bounded queue for pending requests that have no threads to service them. It’s similar to the fixed threadpool, however, the queue_size automatically adjusts according to calculations based on Little’s Law. These calculations will potentially adjust the queue_size up or down by 50 every time auto_queue_frame_size operations have been completed.

The size parameter controls the number of threads.

The queue_size allows to control the initial size of the queue of pending requests that have no threads to execute them.

The min_queue_size setting controls the minimum amount the queue_size can be adjusted to.

The max_queue_size setting controls the maximum amount the queue_size can be adjusted to.

The auto_queue_frame_size setting controls the number of operations during which measurement is taken before the queue is adjusted. It should be large enough that a single operation cannot unduly bias the calculation.

The target_response_time is a time value setting that indicates the targeted average response time for tasks in the thread pool queue. If tasks are routinely above this time, the thread pool queue will be adjusted down so that tasks are rejected.

thread_pool:
    search:
        size: 30
        queue_size: 500
        min_queue_size: 10
        max_queue_size: 1000
        auto_queue_frame_size: 2000
        target_response_time: 1s

scaling

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The scaling thread pool holds a dynamic number of threads. This number is proportional to the workload and varies between the value of the core and max parameters.

The keep_alive parameter determines how long a thread should be kept around in the thread pool without it doing any work.

thread_pool:
    warmer:
        core: 1
        max: 8
        keep_alive: 2m

Allocated processors setting

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The number of processors is automatically detected, and the thread pool settings are automatically set based on it. In some cases it can be useful to override the number of detected processors. This can be done by explicitly setting the node.processors setting.

node.processors: 2

There are a few use-cases for explicitly overriding the node.processors setting:

  1. If you are running multiple instances of Elasticsearch on the same host but want want Elasticsearch to size its thread pools as if it only has a fraction of the CPU, you should override the node.processors setting to the desired fraction, for example, if you’re running two instances of Elasticsearch on a 16-core machine, set node.processors to 8. Note that this is an expert-level use case and there’s a lot more involved than just setting the node.processors setting as there are other considerations like changing the number of garbage collector threads, pinning processes to cores, and so on.
  2. Sometimes the number of processors is wrongly detected and in such cases explicitly setting the node.processors setting will workaround such issues.

In order to check the number of processors detected, use the nodes info API with the os flag.

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